Point Cloud Registration
185 papers with code • 22 benchmarks • 11 datasets
Point Cloud Registration is a fundamental problem in 3D computer vision and photogrammetry. Given several sets of points in different coordinate systems, the aim of registration is to find the transformation that best aligns all of them into a common coordinate system. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis.
Benchmarks
These leaderboards are used to track progress in Point Cloud Registration
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Libraries
Use these libraries to find Point Cloud Registration models and implementationsDatasets
KITTI
3DMatch
DeformingThings4D
3RScan
UrbanLoco
4DMatch
3DCSR dataset
FPv1
FAUST-partial
VBR
Latest papers with no code
OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision
Without relying on learning or any labeled datasets, OptFlow achieves state-of-the-art performance for scene flow estimation on popular autonomous driving benchmarks.
Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration
They typically compute candidate correspondences based on distances in the point feature space.
On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods
This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most $n$ supports and its applications in various AI tasks such as color transfer or domain adaptation.
D3Former: Jointly Learning Repeatable Dense Detectors and Feature-enhanced Descriptors via Saliency-guided Transformer
Notably, tests on 3DLoMatch, even with a low overlap ratio, show that our method consistently outperforms recently published approaches such as RoReg and RoITr.
SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration
To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration
We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation.
A Dynamic Network for Efficient Point Cloud Registration
For the point cloud registration task, a significant challenge arises from non-overlapping points that consume extensive computational resources while negatively affecting registration accuracy.
DiffusionPCR: Diffusion Models for Robust Multi-Step Point Cloud Registration
We propose formulating PCR as a denoising diffusion probabilistic process, mapping noisy transformations to the ground truth.
Zero-Shot Point Cloud Registration
The cornerstone of ZeroReg is the novel transfer of image features from keypoints to the point cloud, enriched by aggregating information from 3D geometric neighborhoods.
Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change
To this end, we introduce the Nothing Stands Still (NSS) benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map.
